February 2019 - graph-tool - archives.skewed.de

How to control the orientation of the graph topology
by Xun Xiao 26 Feb '22

26 Feb '22

Hi Tiago, sometimes, when delete the graph and create a new one, then plot it, the plotted graph is shown upside down (in terms of the node text). So how could we control the orientation of the graph? Or which parameter we should use to always make node text shown in a correct orientation. Thanks a lot. -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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Upside down labels
by charlie 26 Feb '22

26 Feb '22

Hi, I'm suffering from the same issue mentioned in this post: https://git.skewed.de/count0/graph-tool/issues/174 Namely, I'm trying to draw a graph that includes a lot of self-looping edges, and my labels are being printed upside down. If I remove the self-loops the labels are shown the right way up. Is there a fix for it? Thanks, Charlie -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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random graph
by JM 01 Sep '21

01 Sep '21

Dear Tiago, I have a directed graph of about half a million nodes and approximately a million edges following scale free behaviour and a power law degree distribution. To test some of my hypothesis, I would like to generate random smaller graphs (about 50 up to 200 nodes) representative of the big one. When I used a sample function that samples straight away from the real distribution of the big network, I have following problems: - I generate unconnected nodes with both 0 in AND out degree. - I generate small sub parts of a few nodes that are not connected to the main graph. - If only sampling from nodes with at least 1 degree, the generated graph is coherent, but not representative anymore as I need a big portion of nodes with either only one in or one out degree. Here is the part of my script I used for that, where samples are drawn from dictionaries of the degrees: def sample_in(): a=np.random.randint(num) k_in = in_degrees[a] return k_in def sample_out(): if sample_in()==0: b=np.random.randint(num_out) k_out=out_zero_zeros.values()[b] return k_out else: b=np.random.randint(num) k_out=out_degrees[b] return k_out N=200 g=gt.random_graph(N, lambda:(sample_in(), sample_out()), model="constrained-configuration", directed=True) I also tried sampling from a list of tuples as you have mentioned before in the forum, but I didn't receive any results, as the tuples randomly drawn from my list might not be combinable. degs=[(7,1),(4,3),(5,6),(2,4),(6,8),(2,0),(3,5),(0,3),(2,7),(2,1)] g = gt.random_graph(4, lambda i: degs[i], directed=True) - Is there any option I could active that would help me in those cases I described above? - Is there a better way how to create representative small networks? Any help on that issue will be much appreciated. Best wishes, Jana -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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What formula is used for std in vertex_average and edge_average?
by VaSa 16 Jul '20

16 Jul '20

I am curious what is being used to calculate the standard deviation of the average in gt.vertex_average and gt.edge_average >>> t2=gt.Graph() >>> t2.add_vertex(2) >>> t2.add_edge(t2.vertex(0), t2.vertex(1)) >>> gt.vertex_average(t2, "in") (0.5, 0.35355339059327373) Now, shouldn't std be σ(n)=sqrt(((0-0.5)^2+(1-0.5)^2)/2)=0.5 ? also q(n-1)=sqrt((0.5^2+0.5^2)/(2-1))~=0.70710 0.3535 is sqrt(2)/4 which happens to be σ(n-1)/2, so it seems there is some relation to that. A little bigger graph. >>> t3=gt.Graph() >>> t3.add_vertex(5) >>> t3.add_edge(t3.vertex(0), t3.vertex(1)) >>> gt.vertex_average(t3, "in") (0.2, 0.17888543819998318) Now, we should have 0,1,0,0,0 series for vertex incoming degree. So Windows calc gives σ(n)=0.4 and σ(n-1)~=0.44721, so where does 0.1788854 come from ? Reason, I am asking because, I have a large graph, where the average looks quite alright but the std makes no sense, as going by the histogram, degree values are quite a bit more distributed than the std would indicate. -- View this message in context: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com… Sent from the Main discussion list for the graph-tool project mailing list archive at Nabble.com.

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assigning vertex properties when adding edges
by m jadidi 04 Mar '20

04 Mar '20

Hi, I was wondering if there is any way to assign vertex properties while adding edges to the graph. for example using "add_edge_list" I can assign edge properties but later I have to iterate through all vertices again to assign their properties. I know this is not a problem when the vertex property is of the type "int" or "float" because then one can use "vprop.a = values", but in case of "string" and "object" this method doesn't work What would be the best/fastest way to handle this situation. I guess it would be very helpful to extend the "add_edge_list" function to accept vertex property in some way. cheers, -- Mohsen

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Nested Block State hierarchy level after mcmc_equilibrate
by ilyco 16 Jan '20

16 Jan '20

I ran mcmc_equilibrate on a nested block state model in a weighted graph. As per instructions, I copied the initially computed state in another object with increased hierarchy depth to 10. However, this fixed the depth to 10. Everything computed afterwards has depth 10 even if is clear that after 3 or 4 levels the nodes converge to one. There are many empty branches and when I try to plot it with empty_branches = False, I get an error stating it is not a tree. RuntimeError: Invalid hierarchical tree: No path from source to target. Did anybody perform any similar analyses? The hierarchy after mcmc_equilibrate: <NestedBlockState object, with base <BlockState object with 24 blocks (24 nonempty), degree-corrected, with 1 edge covariate, for graph <Graph object, undirected, with 230 vertices and 11230 edges, edges filtered by (<PropertyMap object with key type 'Edge' and value type 'bool', for Graph 0x7fc3a89f1210, at 0x7fc3a64911d0>, False), vertices filtered by (<PropertyMap object with key type 'Vertex' and value type 'bool', for Graph 0x7fc3a89f1210, at 0x7fc3a64912d0>, False) at 0x7fc3a89f1210>, at 0x7fc3a6491950>, and 10 levels of sizes [(230, 24), (24, 5), (5, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1), (1, 1)] at 0x7fc3a6491590> -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Computing K number of shortest paths for a single end-node
by sergzz 01 Mar '19

01 Mar '19

Hello, I have just discovered graph-tool and read through a bunch of documentation. I am fairly new to python and graphs. The speed comparison to networkx really impressed me! I was utilizing networkx for a project to find K number of weighted shortest paths from a source to a target node. It has 'all_simple_path' function - outputting a generator that generates all paths starting from shortest to longest. I would simply cut the generator to the K number of paths that I needed. I can't find a similar function in graph-tool, but I am sure this could be implemented. The only thing I found was the all_shortest_paths function, but that only returns the shortest path(s), in most cases only 1. Could you point me in the right direction how to efficiently generate and store K number of shortest paths for a specified node? I know I could just generate all paths and then cut down based on order, but that's not a real solution since I am running into memory issues with a small amount of nodes. Thanks! -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Weighted SBM | weight prediction
by Adrien Dulac 28 Feb '19

28 Feb '19

Dear all, I am a bit confused about the use of the weighted network models for a weight prediction task; Suppose we have a weighted network where edges are integers. We fit a SBM with a Poisson kernel as follows: |data = gt.load_graph(...) # The adjacency matrix has integer entries, and weights greater than zero are stored in data.ep.weights. state = gt.inference.minimize_blockmodel(data, B_min=10, B_max=10, state_args= {'recs':[data.ep.weights], 'rec_types' : ["discrete-poisson"]}) | My question, is how can we obtain, from |state|, a point estimate of the Poisson parameters in order to compute the distribution of the weights between pairs of nodes. Regards, Adrien Dulac

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Graphviz only showing single edge to node even with multiple edges
by Rich5 28 Feb '19

28 Feb '19

I'm having trouble with getting graphviz_draw to display the same edges as graph_draw. graph_draw displays the correct number of edges. (Please ignore the color differences. I'm not concerned with that just now) Using the same graph the following calls generate graphs with different topologies: graph_draw(g, vertex_text=g.vp.label, output_size=(500,500)) <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> graphviz_draw(g, #vsize=.00001, size=(1000,1000), ratio="auto", layout="dot", splines=True, penwidth=1, vcolor=g.vp.x_in, ecolor=g.ep.score, vprops={"label": g.vp.label}, vcmap=cm.OrRd, ecmap=cm.OrRd, eprops={ "arrowhead": "normal", "arrowsize" : 2.0, }, gprops={"label" : g.gp.x_in, }, overlap="prism", output='network.png') <http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com…> All filters have been cleared prior to calling these functions. Any ideas what I'm doing wrong? I'm fairly new to using graph-tool and graphviz. -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/

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Fwd: Generate scale-free networks with desired power-law degree distributions
by Weitao Han 28 Feb '19

28 Feb '19

I'm trying to reproduce the synthetic networks (graphs) described in some papers. How to create scale-free networks with desired power-law degree distributions, ? The lambda λ values are given, like: 1. n = 50000, λ = 3, 2.7, 2.3, with in a paper 2. n = 4000 and λ = 2.5, or n = 6000 and λ = 3 in the other paper Here are some references: 1. Catastrophic cascade of failures in interdependent networks, Buldyrev et al. 2010, with a separately provided Supplementary Information 2. Small Cluster in Cyber Physical Systems, Huang et al. 2014 3. Catastrophic cascade of failures in interdependent networks, Havlin et al. 2010, this is on the Arxiv and somewhat clarifies the first

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